Desired Relative Distance Model-based Personalized Braking Algorithm for One-pedal Driving of Electric Vehicles
Kyoung Hyun Kwak, Yu He, Youngki Kim, Yue Ming Chen, Shihong Fan, Justin Holmer, Jason H. Lee
Abstract
One-pedal driving (OPD) provides a unique experience to a driver during car-following as an important feature for energy saving in electric vehicles. However, the braking behavior of one driver may differ significantly from that of another; therefore, it is necessary to develop a personalized braking algorithm to reduce unpleasantness for the passengers. In addition, for ease of implementation in a real vehicle, a relatively simple structure is preferred. This paper proposes a desired relative distance-based personalized braking (DRD-PB) algorithm to achieve these requirements. The desired relative distance model is formulated as a function of the ego vehicle speed and the relative speed in a braking scenario in which a preceding vehicle exists. The proposed algorithm is calibrated and validated against single driver-driven data collected in various driving conditions. The simulation results show that the proposed algorithm can achieve satisfactory braking performance close to that of a human driver: the median of the root mean square errors of drel, vego, and aego are 4.3 m, 0.95 m/s, and 0.46 m/s2, respectively.